Studying predator foraging mode and hunting success at the individual level with an online videogame
Bibliographic record
Abstract
Abstract Predator–prey interactions are important drivers of community and ecosystem dynamics. With an online multiplayer videogame, we propose a novel system to explore within population variation in predator hunting mode, and how predator–prey behavioral interactions affect predator hunting success. We empirically examined how four predator foraging behaviors covary at three hierarchical levels (among environments, among individuals, and within individuals) to assess the structure of predator hunting mode. We also investigated how prey activity affects the foraging behavior and hunting success of predators. Our study supports key findings on predator foraging mode and predator-prey interactions from behavioral ecology. We found that individual predators displayed a diversity of hunting tactics that were conditioned by prey behavior. With prey movement, individual predators specialized either as cursorial or ambush hunters along a continuum of their hunting traits, but also shifted their strategy between encounters. Both types of hunters were generally better against slower moving prey, and they achieved similar prey captures over the sampling period. This suggests that virtual worlds supporting multiplayer online videogames can serve as legitimate systems to advance our knowledge on predator–prey interactions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".